Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

Water-soluble organic aerosol (WSOA) in fine particles (PM2.5) collected during wintertime in a polluted city (Handan) in Northern China was characterized using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS). Through comparing with real-time measurements from a collocated Aerosol Chemical Speciation Monitor (ACSM), we determined that WSOA on average accounts for 29% of total organic aerosol (OA) mass and correlates tightly with secondary organic aerosol (SOA; Pearson's r = 0.95). The mass spectra of WSOA closely resemble those of ambient SOA, but also show obvious influences from coal combustion and biomass burning. Positive matrix factorization (PMF) analysis of the WSOA mass spectra resolved a water-soluble coal combustion OA (WS-CCOA; O/C = 0.17), a water-soluble biomass burning OA (WS-BBOA; O/C = 0.32), and a water-soluble oxygenated OA (WS-OOA; O/C = 0.89), which account for 10.3%, 29.3% and 60.4% of the total WSOA mass, respectively. The water-solubility of the OA factors was estimated by comparing the offline AMS analysis results with the ambient ACSM measurements. OOA has the highest water-solubility of 49%, consistent with increased hygroscopicity of oxidized organics induced by atmospheric aging processes. In contrast, CCOA is the leastwater soluble, containing 17% WS-CCOA. The distinct characteristics of WSOA from different sources extend our knowledge of the complex aerosol chemistry in the polluted atmosphere of Northern China and the water-solubility analysis may help us to understand better aerosol hygroscopicity and its effects on radiative forcing in this region. (C) 2020 Published by Elsevier B.V.Peer reviewe

Assessing the effects of trans-boundary aerosol transport between various city clusters on regional haze episodes in spring over East China

Regional haze episodes have been frequently reported in east China since 2000. In the present study, two regional haze episodes over east China in the spring of 2011 were examined by observations and simulations conducted by a three-dimensional regional chemical transport model (NAQPMS) with an on-line tracer-tagged module. The model reproduced accurately the observed PM2.5 with correlation coefficient ranging from 0.52 to 0.76 and root mean square error (RMSE) of 20–50µg/m3 in four city clusters (Yangtze River Delta, Shandong Peninsula, Huabei Plain and Central Liaoning) over east China. Our results indicate that a northward cross-border transport from the Yangtze River Delta to Central Liaoning below 2 km above ground played an important role in the formation of these regional high PM2.5 episodes. Contributions of regional transport from outside city clusters presented an increasing trend from south to north. In the northernmost cluster (Central Liaoning), the contribution from other city clusters reached 40–50% during the two episodes. In contrast, it was below 10% in the Yangtze River Delta (southernmost cluster). Mixing accumulation of pollutants from various city clusters during transport was responsible for this trend. Furthermore, a preliminary estimate shows that cross-border transport of PM2.5 might increase 0.5–3% daily mortality during the high PM2.5 episodes

NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components

PM2.5, a complex mixture with diverse chemical components, exerts significant impacts on the environment, human health, and climate change. However, precisely describing spatiotemporal variations of PM2.5 chemical components remains a difficulty. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (NAQPMS-PDAF v1.0) that is suboptimal for chemical components. This paper introduces a novel hybrid nonlinear chemical DA system (NAQPMS-PDAF v2.0) to accurately interpret key chemical components (SO42-, NO3-, NH4+, OC, and EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handing and balancing stability and nonlinearity in chemical DA, which is achieved by incorporating the non-Gaussian-distribution ensemble perturbation and hybrid Localized Kalman-Nonlinear Ensemble Transform Filter with an adaptive forgetting factor for the first time. The dependence tests demonstrate that NAQPMS-PDAF v2.0 provides excellent DA results with a minimal ensemble size of 10, surpassing previous reports and v1.0. A one-month DA experiment shows that the analysis field generated by NAQPMS-PDAF v2.0 is in good agreement with observations, especially reducing the underestimation of NH4+ and NO3- and the overestimation of SO42-, OC, and EC. In particular, the CORR values for NO3-, OC, and EC are above 0.96, and R2 values are above 0.93. NAQPMS-PDAF v2.0 also demonstrates superior spatiotemporal interpretation, with most DA sites showing improvements of over 50 %–200 % in CORR and over 50 %–90 % in RMSE for the five chemical components. Compared to the poor performance in global reanalysis dataset (CORR: 0.42–0.55, RMSE: 4.51–12.27 µg/m3) and NAQPMS-PDAF v1.0 (CORR: 0.35–0.98, RMSE: 2.46–15.50 µg/m3), NAQPMS-PDAF v2.0 has the highest CORR of 0.86–0.99 and the lowest RMSE of 0.14–3.18 µg/m3. The uncertainties in ensemble DA are also examined, further highlighting the potential of NAQPMS-PDAF v2.0 for advancing aerosol chemical component studies